Karl Rupp <[email protected]> writes: > However, for Gram-Schmidt you can just compute all the > necessary scalar products at the same time (VecMDot) and reuse the > common data vector. This gives you a speed-up of a factor of almost two.
It's not a factor of 2, it's a factor of k where k is the size of the subspace. Classical Gram-Schmidt needs one reduction per iteration (normalization can be hidden), but modified needs k reductions.
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